JP4016234B2 - Image processing method and image processing apparatus - Google Patents

Description

[0001]
BACKGROUND OF THE INVENTION
The present invention relates to an image processing method and an image processing apparatus for compressing or expanding a color gamut in accordance with a color gamut of a color image output device such as a color printer or a color display.
[0002]
[Prior art]
A color image output device such as a color printer or a color display cannot reproduce all colors that can be perceived by humans, and can reproduce only a limited range of colors called a color gamut (color reproduction range, gamut). On the other hand, since there is no limitation on the range that can be represented by image data in a color input image, there is a possibility that a color outside the color gamut of the output device is input as the input image. A process of converting into the color gamut of the output device, that is, a process of compressing the color gamut (color expression range) of the input image into the color gamut of the output device is required.
[0003]
On the other hand, when the input image is included in the color gamut of the output device, the input image can be made to be a higher quality image by effectively using the color gamut of the output device, for example, by increasing the saturation. It is also possible to perform a process called color gamut expansion to convert to.
[0004]
As conventional techniques relating to color gamut compression, three methods described in the International Color Consosium Format proposed by the International Color Consosium, Perceptual, Saturation, and Colorimetric are well known.
[0005]
Perceptual changes not only the color outside the color gamut of the output device but also the color within the color gamut at a certain ratio while preserving the hue, and compresses the color gamut toward a fixed point on the gray axis within the color gamut of the output device. It is a method to do.
[0006]
Saturation is a method of pasting the output device color gamut outline to a fixed point on the gray axis in the output device color gamut while keeping the hue only for colors outside the output device color gamut. In this method, only the color outside the device gamut is pasted to the color gamut outline of the output device toward the gray axis in the color gamut of the output device while preserving brightness and hue.
[0007]
[Problems to be solved by the invention]
However, Perceptual compresses the input image at a constant rate regardless of the hue and lightness of the input image. Therefore, there is a problem that the image is compressed to a hue or lightness region that does not require compression.
[0008]
Saturation and Colorimetric also paste and compress colors outside the output device's color gamut into the output device's color gamut outline regardless of the hue and brightness of the input image. When a color similar to the color after conversion into the color gamut outline is present in the input image of the original image within the color gamut of the output device, the discrimination between the two colors deteriorates and the output device When there is a gradation outside the color gamut, there is a problem that the gradation may be lost by these processes.
[0009]
Furthermore, these Saturations or Colorimetrics are indexed into a multi-dimensional DLUT (Direct Lookup Table) using the upper bits of the input image data as addresses, and the read grid point data is interpolated using the lower bits of the input image data. In the case where the output image data is obtained by a multi-dimensional DLUT interpolation type color converter, the grid point data outside the color gamut of the output device is pasted to the color gamut contour of the output device. If determined, there is a problem that colors in the color gamut close to the color gamut outline of the output device are unnecessarily compressed.
[0010]
To deal with these problems, (1) a method of selecting a conversion method considered most preferable from the above three types depending on the type of document such as a photograph or a graph used in business, (2) A method of counting the number of pixels in and out of the color gamut of the output device in the input image and selecting a conversion method according to the ratio, as shown in Japanese Patent No. 162218, (3) A method has been proposed in which the number of pixels in and out of the color gamut of the output device in the input image is counted as shown in Japanese Patent No. 274997 and the compression amount is continuously changed according to the ratio. .
[0011]
However, these methods essentially use the above-described Perceptual, Saturation, or Colorimetric, and thus cannot fundamentally solve the above-described problem.
[0012]
Further, Japanese Patent Laid-Open No. 7-203234 discloses that a uniform color space is divided into unit areas, the number of pixels included in the input image is obtained for each unit area, and is outside the color gamut (color reproduction range) of the output device. For the unit area, a method of mapping to another unit area having the same brightness according to the number of pixels is shown.
[0013]
However, since this method performs color mapping only for unit regions outside the color gamut of the output device, for example, for gradations that continue from the color gamut of the output device to the color gamut, the color of the region outside the color gamut of the output device There is a problem in that the color continuity is lost by the conversion, and a desired gradation cannot be expressed.
[0014]
In addition to the above problems, as a color gamut compression method corresponding to the visual curve of the hue line of the color space, reported in Color Forum Japan '95 Proceedings 4-1 (p.45-48), Japanese Patent Application Laid-Open No. 7-203234 discloses that processing is performed by dividing a color gamut into small blocks.
[0015]
However, this method has a problem that it cannot cope with a visual curve of a hue line in a small block and cannot ensure continuity between blocks.
[0016]
In view of the above, an object of the present invention is to realize the following points. (1) Depending on the distribution of the input image, only the area that requires color gamut compression can be color gamut compressed. (2) The direction of color gamut compression can be continuously adjusted for each region. (3) The amount of color gamut compression can be continuously adjusted including pasting. (4) When color gamut compression is performed by a multi-dimensional DLUT interpolation type color converter, it is possible to avoid excessively unnecessary color gamut compression especially during pasting.
[0018]
[Means for Solving the Problems]
The image processing method of the first invention is
In an image processing method for converting a color input image or a partial area thereof according to a color gamut of a color image output device,
A base line, which is a straight line connecting the fixed point in the color gamut of the color image output apparatus uniquely determined from the conversion target point and the conversion target point, and an outermost curved surface of the color gamut of the color image output apparatus or a closed in the color gamut. An output contour point calculating step for obtaining an output contour point that is an intersection with the output contour surface that is a curved surface;
An input outermost point calculation step for obtaining an input outermost point that is an intersection of the baseline and the input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
A weighting factor determination step for determining a first weighting factor and a second weighting factor from the output contour point and the input outermost contour point;
A first conversion point calculating step of calculating a first conversion point by compressing or expanding the conversion target point to a point on the baseline using the fixed point, the output outline point, and the first weighting factor;
A correction point calculating step of correcting the conversion target point with correction data corresponding to the conversion target point and calculating a correction point;
Second conversion point calculation for calculating a second conversion point by compressing or expanding the correction point to a point on a correction line, which is a straight line connecting the correction point and the fixed point, using the first weighting factor. Process,
Using the first conversion point, the second conversion point, and the second weighting factor, a point on a line segment connecting the first conversion point and the second conversion point is converted into a final conversion for the conversion target point. A final conversion point calculation step of calculating as points,
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting coefficient when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of, determined from at least two parameters,
The second weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the second weighting factor as an output. The shape of the one-variable function is set so that the coordinates of the first transformation point and the final transformation point are the same up to some percent of the distance from the fixed point to the input outermost contour point. And a fifth parameter for determining what percentage or more of the distance from the fixed point to the input outermost point is set so that the coordinates of the second conversion point and the final conversion point are the same. And determined from at least two parameters,
It is characterized by that.
[0019]
The image processing method of the second invention is
In an image processing method for converting a color input image or a partial area thereof according to a color gamut of a color image output device,
A correction point calculating step of correcting the conversion target point with correction data corresponding to the conversion target point and calculating a correction point;
A correction base line that is a straight line connecting a fixed point in the color gamut of the color image output apparatus and the correction point, which is uniquely determined from the conversion target point, and an outermost curved surface of the color gamut of the color image output apparatus or in the color gamut An output contour point calculating step for obtaining an output contour point that is an intersection with the output contour surface that is a closed curved surface;
An input outermost point calculation step for obtaining an input outermost point that is an intersection between the correction baseline and the input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
A weighting factor determination step of determining a first weighting factor that is a ratio of compressing or expanding the correction point from the output outermost point and the input outermost point;
A first conversion point calculating step of calculating a first conversion point by compressing or expanding the correction point to a point on the correction baseline using the fixed point, the output contour point, and the first weighting factor;
A final conversion point calculating step of correcting the first conversion point with reverse correction data corresponding to the first conversion point and calculating a final conversion point for the conversion target point;
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting coefficient when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of determining at least two parameters,
It is characterized by that.
[0021]
[Action]
Image processing method according to the first invention Then, the conversion target point is compressed or expanded on the base line (a straight line connecting the fixed point and the conversion target point) to obtain the first conversion point, and the correction point calculated from the conversion target point based on the correction data is the correction line (fixed point). The second conversion point is obtained by compressing or expanding on the straight line connecting the correction point and the correction point, The final transformation point as a point on the line segment connecting the first transformation point and the second transformation point Therefore, the color gamut conversion as shown in the above (1) to (4) can be realized. .
[0022]
In the image processing method according to the second aspect of the present invention, the correction point calculated by the correction data from the conversion target point is compressed or expanded on the correction baseline (a straight line connecting the fixed point and the correction point), and First Calculate the conversion point First The transformation point First Since the final conversion point is calculated by correcting with the reverse correction data corresponding to the conversion point, the final conversion point can be obtained on an ideal conversion curve passing through the conversion target point and the fixed point. Similar to the image processing method, color gamut conversion as shown in the above (1) to (4) can be realized.
[0025]
DETAILED DESCRIPTION OF THE INVENTION
In the CIELAB (L * a * b *) color space, a color input image (hereinafter simply referred to as an input image) or a partial area thereof is color gamut compressed or color gamut expanded as an example. Show. However, the present invention can also be applied to other color spaces such as CIE LUV (L * u * v *), YIQ, YCbCr, XYZ, and RGB.
[0026]
[Outline of the Method of the Invention]
An outline of the method of the first invention will be described with reference to FIG. 1 or FIG. FIGS. 1 and 2 show the CIELAB color space two-dimensionally for the sake of convenience with the L * axis that is the brightness axis and the C * axis that is the saturation axis.
[0027]
1 and 2, a point P0 is a fixed point in the color gamut of a color image output device (hereinafter simply referred to as an output device) and is set on the gray axis, that is, on the L * axis in the CIELAB color space, as will be described later. To do. Hereinafter, the fixed point is a fixed point P0 in the color gamut of the output device.
[0028]
The point P1 is a conversion target point represented by a certain color coordinate value of the input image or its partial area. The line L1 is a line from the fixed point P0 toward the conversion target point P1, and is defined as a base line.
[0029]
The curved surface C2 is an outermost curved surface in the color gamut of the output device or a closed curved surface in the color gamut of the output device set as described later, and is defined as an output outer curved surface. The curved surface C3 is the outermost curved surface of the color gamut of the input image or its partial region, and is defined as the input outermost curved surface. FIGS. 1 and 2 show the case where the input outermost curved surface C3 is outside the output outer curved surface C2 and the transformation target point P1 is compression-transformed. Conversely, the output outer curved surface C2 is outside the input outermost curved surface C3. Thus, the method of the present invention can also be used when the transformation target point P1 is extended and transformed.
[0030]
Point P2 is defined as an output contour point at the intersection of the base line L1 and the output contour surface C2. Point P3 is defined as the input outermost point at the intersection of the base line L1 and the input outermost surface C3.
[0031]
The point P4 is a first conversion point, and is obtained by weighted averaging the fixed point P0 and the output contour point P2 using the first weighting factor. Therefore, the first conversion point P4 is a point on the baseline L1. The point P5 is a second conversion point and is obtained using correction data for correcting the compression direction or the expansion direction and the first weighting factor. The line L5 is a line from the fixed point P0 toward the second conversion point P5 and is defined as a correction line.
[0032]
In FIG. 1 and FIG. 2, the correction line L5 is different from the base line L1, but if the correction data is zero, the correction line L5 coincides with the base line L1. The correction data is set and prepared as discrete data in a color space, for example, as shown as correction data D1 in FIGS.
[0033]
The point P6 is the final conversion point for the conversion target point P1, and is obtained by weighted averaging the first conversion point P4 and the second conversion point P5 using the second weighting factor. Therefore, the final conversion point P6 is a point on the line segment connecting the first conversion point P4 and the second conversion point P5.
[0034]
Using FIG. 18 and FIG. Second The outline of the method of the present invention will be described. FIG. 18 shows the CIELAB color space two-dimensionally with the L * axis as the lightness axis and the C * axis as the saturation axis, and FIG. 19 shows the CIELAB color space with the a * axis and b *. It is shown two-dimensionally with an axis.
[0035]
As already described in the first invention, also in FIGS. 18 and 19, the point P0, the point P1, the curved surface C2, and the curved surface C3 are a fixed point, a conversion target point, an output outer curved surface, and an input outermost curved surface, respectively. .
[0036]
The point P21 is a point calculated by applying correction data corresponding to the conversion target point P1 to the conversion target point P1, and is defined as a correction point. The correction data is prepared as discrete vector data in the color space, and correction data corresponding to a necessary point can be calculated by interpolation or the like.
[0037]
The straight line L2 is a straight line connecting the correction point P21 and the fixed point P0, and is defined as a correction base line.
[0038]
A curve L3 is a curve that passes through the conversion target point P1 and the fixed point P0, and is defined as a virtual conversion line. The correction data is calculated based on this virtual conversion line L3. The virtual conversion line L3 is determined according to the purpose. For example, when the purpose is to correct the bending of the hue line, the equal hue line passing through the conversion target point P1 and the fixed point P0 is the virtual conversion line. It becomes.
[0039]
Point P22 is defined as the input outermost point at the intersection of the corrected baseline L2 and the input outermost curved surface C3. A point P23 is defined as an output outline point at the intersection of the corrected baseline L2 and the output outline surface C2.
[0040]
Point P24 is a point where compression or expansion in accordance with the color gamut of the output device is performed on correction point P21 along correction base line L2, and is defined as a conversion point. The compression or expansion processing according to the color gamut of the output device may be performed using a first weighting factor determined from the fixed point P0, the input outermost contour point P22, the output outer contour point P23, and the correction point P21. You may carry out by the method of.
[0041]
The point P25 is a final conversion point for the conversion target point P1, and is calculated by applying reverse correction data corresponding to the conversion point P24 to the conversion point P24. This reverse correction data is obtained by switching the start point and end point of a vector of correction data.
[0042]
Other points, curved surfaces, and curves shown in FIGS. 1, 2, 18, and 19 will be described later.
[0043]
[Set fixed point]
The fixed point P0 is a point that changes continuously as a fixed point on the gray axis in the color gamut of the output device, or as a function such as the brightness and saturation of the conversion target point P1, or the brightness of the conversion target point P1. It is set as a point that is discretely different depending on the saturation and the like.
[0044]
FIG. 3 shows an example in which the fixed point P0 continuously changes as a function such as brightness and saturation of the conversion target point P1. This shows the relationship between the angle Φ and the L * value of the fixed point P0, where Φ is the angle formed by the vector from the conversion target point P1 to the fixed point P0 with respect to the a * b * plane. When the fixed point P0 is defined, the relationship between the fixed point P0 and the conversion target point P1 is, for example, as shown in FIG.
[0045]
In this way, when the fixed point P0 is a point that changes continuously as a function of the brightness and saturation of the conversion target point P1, or a point that is discretely different depending on the brightness and saturation of the conversion target point P1, etc. The base line L1 from the fixed point P0 to the conversion target point P1 needs to be prevented from intersecting in the entire area in the color space as shown in FIG. 4, and when this condition is not satisfied, the base line L1 is compressed or expanded. May cause gradation reversal.
[0046]
When the fixed point P0 is a function of only the lightness of the conversion target point P1, if the angle Φ is 0 degree, the gradation is not reversed, but if the angle Φ is other than 0 degrees, the gradation is reversed. When the brightness of the conversion target point P1 is outside the color gamut of the output device, it is difficult to set the fixed point P0 within the color gamut of the output device. The fixed point P0 is set in consideration of these points.
[0047]
[Output contour surface]
The output outer curved surface C2 may normally be the outermost curved surface of the color gamut of the output device. However, when the output device is a display or the like and the white point of the input image is different from the white point of the output device, it may be desirable to set the closed surface in the color gamut of the output device as the output contour surface C2. .
[0048]
FIG. 5 schematically shows an example of the relationship between the color gamut of the input image or its partial region and the color gamut of the output device, where the curved surface C20 is the outermost curved surface of the color gamut of the output device, and the curved surface C3 is the input outermost curved surface. That is, the outermost curved surface of the color gamut of the input image or its partial area.
[0049]
In this example, when the white point P1w of the input image or its partial region is converted into the compression destination P6w on the gray axis in the color gamut of the output device, the lightness higher than that of the white point compression destination P6w in the color gamut of the output device. Therefore, depending on the compression method, the other color of the input image or its partial area may be converted to a color P6o having a higher brightness than the white point compression destination P6w, giving an unnatural impression.
[0050]
In such a case, a curved surface including only a lightness area lower than the white point compression destination P6w may be set as the closed curved surface in the color gamut of the output device, and this may be used as the output contour surface C2. For example, in FIG. 5, as the saturation increases from the white point compression destination P6w as a starting point to the closed curved surface C21 extending horizontally so that the lightness is the same as the white point compression destination P6w, or from the white point compression destination P6w as the starting point, A closed curved surface C22 whose brightness is gradually decreased is set as a closed curved surface in the color gamut of the output device, and is defined as an output contour curved surface C2.
[0051]
When the saturation is increased, it is visually perceived brighter than gray of the same brightness, but if the latter closed curved surface C22 is the output contour surface C2, it is possible to prevent such a visual phenomenon from giving an unnatural impression. can do.
[0052]
As a countermeasure against such a visual defect, it is conceivable to perform color gamut conversion after uniformly converting only the lightness for all colors of the input image or its partial area. However, when not only the color gamut compression but also the color gamut expansion occurs, the same problem occurs at the time of expansion. Therefore, even when the brightness is converted in advance, a closed surface such as the above-described closed surface C22 is used as the output contour surface C2. It is desirable to set.
[0053]
[First Embodiment as Image Processing Method]
FIG. 6 shows a color coordinate value of a certain point, that is, a certain pixel of an input image or a partial area thereof as a conversion target point P1, and converts this to a final conversion point P6 of a color coordinate value suitable for the color gamut of the output device. 1 shows an embodiment of the first invention.
[0054]
The method of FIG. 6 can be realized on a computer by a processing program, but some steps can also be performed by hardware. This method generally includes an output contour point calculation step 10, an input outermost contour point calculation step 20, a first weighting factor determination step 30, a second weighting factor determination step 40, a first conversion point calculation step 50, and a second conversion point. It comprises a calculation step 60 and a final conversion point calculation step 70. First, the output outline point calculation step 10 or the input outermost point calculation step 20, for example, the output outline point calculation step 10 is performed.
[0055]
(Output outline point calculation process)
In the output contour point calculation step 10, from the information (color coordinate value) S1 of the conversion target point P1, the information (color coordinate value) S0 of the fixed point P0, the information SC2 of the output contour surface C2, and the output contour point calculation parameter PP2, Information S2 of the output contour point P2, which is the intersection of the base line L1 that is a line from the fixed point P0 toward the conversion target point P1 and the output contour surface C2, is calculated.
[0056]
FIG. 7 shows a specific example of the output contour point calculation step 10. In this example, first, in the output contour representative point preparation step 11, as shown in FIG. 1 and FIG. 2 based on the output contour surface information SC 2. Information (color coordinate values) S12 of a plurality of output outline representative points P12 is calculated.
[0057]
The output contour surface information SC2 here is a discrete data pair of the relationship between the input signal dependent on the output device and the output color, and the output contour surface C2 is a closed surface in the color gamut of the output device. Is information on the shape.
[0058]
As the discrete data pairs, for example, when the output device is a printer, various YMC (K)% combination data, color LAB data when the combination is printed, and when the output device is a display In other words, data of various combinations of RGB and LAB data or XYZ data of colors displayed by the combination may be prepared.
[0059]
Based on these data, the predicted value is set to a point on the outermost curved surface of the color gamut of the output device, and the color that is the predicted value is predicted.
[0060]
Setting the predicted value to a point on the outermost curved surface of the color gamut of the output device means that, for example, when the output device is a three-color printer, a combination of M% and C% when Y% is zero This corresponds to preparing data of a combination of C% and Y% when M% is zero, and a combination of Y% and M% when C% is zero.
[0061]
Further, when the output contour surface C2 is a closed curved surface in the color gamut of the output device, the color data on the outermost contour surface of the color gamut of the output device is corrected using the information on the shape, and the output contour surface C2 is corrected. The representative point information is S12.
[0062]
The prediction method here is to predict the color output from an input signal dependent on an arbitrary output device based on the discrete data pair of the relationship between the input signal dependent on the output device and the output color. A known method can be used. Furthermore, if the method proposed in Japanese Patent Application No. 9-4949 (filed on Jan. 14, 1997) is used, the prediction can be made with high accuracy.
[0063]
In the method of Japanese Patent Application No. 9-4949 (hereinafter referred to as the prior application), the relationship between the input signal and the output signal is linked by a linear determinant including a constant term, and this determinant is obtained from a plurality of actual input signal data. The weighted coefficient, the determinant coefficient, and the predicted value are successively applied so that the sum of squares of the weighted Euclidean distance between the output predicted value obtained using the output data and the corresponding actual output signal data is minimized. It is determined by an approximation method.
[0064]
In the output outline point calculation step 10 of FIG. 7, next, in the polar coordinate conversion step 12, the conversion target point information S1 and the output outline representative point information S12 are respectively converted into polar coordinates around the fixed point P0 based on the fixed point information S0. To obtain conversion target point polar coordinate information S1r and output outline representative point polar coordinate information S12r.
[0065]
Next, in the output outline point calculation final step 13, based on the conversion target point polar coordinate information S1r, the output outline representative point polar coordinate information S12r, and the output outline point calculation parameter PP2, information on the output outline point P2 (color coordinate value) ) Calculate S2.
[0066]
Here, assuming that the transformation target point polar coordinates are O (ro, θo, φo) and the output contour representative point polar coordinates are GOUTi (ri, θi, φi), i = 1, Nout, the base line L1 and the output contour surface C2 Is calculated as a predicted value (ro) based on N (θi, φi) and ri data pairs, with the predicted value being (θo, φo). Therefore, it can be calculated using a known method or the method of the prior application.
[0067]
In this case, since it is polar coordinates, pay attention to the continuity with respect to θ and φ, for example, that 0 ° and 360 ° of θ are continuous, and calculate the output contour point according to the number of output contour representative points P12. The parameter PP2 may be determined.
[0068]
(Input outermost point calculation process)
In the entire process shown in FIG. 6, next, in the input outermost point calculation step 20, the conversion target point information S1, the fixed point information S0, the pixel value information S20 of the input image or its partial area, the input outermost point calculation parameter Based on PP3, information S3 of the input outermost contour point P3, which is the intersection of the base line L1 and the input outermost contour surface C3, is calculated.
[0069]
As the pixel value information S20 of the input image or its partial area, the individual pixel value itself of the input image or its partial area may be used. However, the color gamut of the input image or its partial area is set in advance, for example, NTSCRGB color gamut. , An SRGB color gamut, or a color gamut represented by a chromaticity point of YMCBRGWK, etc., discrete chromaticity information located in the outermost part of the assumed color gamut may be used.
[0070]
In the case of using discrete chromaticity information located in the outermost portion of the color gamut assumed in advance, the input outermost contour representative point candidate selection step and the input outermost contour representative point selection step described below can be omitted. .
[0071]
FIGS. 8 and 9 show specific examples of the input outermost contour point calculation step 20, respectively. The example of FIG. 8 is a case where the input outermost contour representative point is selected from all pixel values of the input image or its partial area. The example of FIG. 9 is a case where the input outermost representative point candidate is obtained by thinning out the pixels of the input image or its partial region, and the input outermost representative point is selected from the input outermost representative point candidate.
[0072]
In the example of FIG. 8, first, in the polar coordinate conversion step 22, the pixel value information S20 of the input image or its partial area is converted into polar coordinates centered on the fixed point P0 based on the fixed point information S0, and the pixel value polar coordinates are converted. Information S20r is obtained. At this time, similarly, based on the fixed point information S0, the conversion target point information S1 is converted into polar coordinates centered on the fixed point P0 to obtain the conversion target point polar coordinate information S1r. Since it has already been calculated in the polar coordinate conversion step 12 in step 10, this may be used.
[0073]
Next, in the input outermost contour representative point selection step 23, information on the input outermost contour representative point P13 which is a discrete point on the input outermost contour surface C3 as shown in FIGS. 1 and 2 is obtained from the pixel value polar coordinate information S20r. S13 is elected.
[0074]
Specifically, if the pixel value polar coordinates of the input image or its partial region are INi (ri, θi, φi), i = 1, Nin, the (θ, φ) plane is divided into meshes and INi (ri, θi, .phi.i) is distributed, data having the maximum (r) is searched for in each mesh, and input outermost contour representative points GINi (ri, .theta.i, .phi.i), i = 1, Nin may be used.
[0075]
Next, in an input outermost point calculation final step 24, based on the transformation target point polar coordinate information S1r, the input outermost representative point information S13, and the input outermost point calculation parameter PP3, the baseline L1 and the input outermost curved surface are obtained. Information (color coordinate value) S3 of the input outermost contour point P3, which is an intersection with C3, is calculated.
[0076]
Here, since the transformation target point polar coordinates are O (ro, θo, φo) and the input outermost contour representative point P13 is GINi (ri, θi, φi), i = 1, Nin, the base line L1 and the input outermost contour surface. Calculating the intersection with C3 results in the problem of predicting the predicted value (ro) based on N (θi, φi) and ri data pairs, with the predicted value being (θo, φo). . Therefore, it can be calculated using a known method or the method of the prior application.
[0077]
In this case, since it is polar coordinates, it is noted that 0 ° and 360 ° are continuous for θ and φ, and the input outermost point calculation parameter PP3 can be determined according to the number of input outermost representative points P13. That's fine. However, the input outermost curved surface C3, which is the outermost curved surface of the color gamut of the input image or its partial region, may have more unevenness than the case of calculating the output contour point information S2, and in this case, smoothing is performed. The point is to set the input outermost point calculation parameter PP3 so as to enhance the effect.
[0078]
In the example of FIG. 9, first, in the input outermost contour representative point candidate selection step 21, input outermost contour representative point candidate information S <b> 21 is obtained from the pixel value information S <b> 20 of the input image or its partial region.
[0079]
In the case of selecting the input outermost representative point from the input image or the pixel value information S20 of the partial area as in the example of FIG. 8, the processing in the polar coordinate conversion step 22 and the input outermost representative point selection step 23 takes time. However, by selecting input outermost contour representative point candidates in advance in the input outermost contour representative point candidate selection step 21 as in the example of FIG. 9, in the subsequent polar coordinate conversion step 22 and input outermost contour representative point selection step 23. The processing time can be shortened. This selection of input outermost contour representative point candidates is simply performed by thinning out the pixels of the input image or its partial area, or obtaining an average value of several pixels, and using the average value as input outermost contour representative point candidate information S21. Such a method can be used.
[0080]
Thereafter, similarly to the example of FIG. 8, the intersection of the base line L1 and the input outermost contour surface C3 is obtained through the polar coordinate transformation step 22, the input outermost contour representative point selection step 23, and the input outermost contour point calculation final step 24. Information S3 of the input outermost contour point P3 is obtained.
[0081]
(First weighting factor determination step)
In the entire process shown in FIG. 6, next, in a first weighting factor determination step 30, conversion target point information S1, fixed point information S0, output contour point information S2, input outermost contour point information S3, and first weighting factor determination Based on the parameter PW1, the first weighting factor information SW1 used in the subsequent first conversion point calculation step 50 and second conversion point calculation step 60 is calculated.
[0082]
In the first weighting factor determination step 30, a one-variable function calculation is performed with the ratio between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. To determine the first weighting factor. The shape of the one-variable function will be described with reference to FIGS.
[0083]
FIG. 10 shows one variable when the ratio R2 between the distance from the fixed point to the output outline point and the distance from the fixed point to the input outermost outline point is 1 or less, that is, when compression is performed as shown in FIGS. The shape of the function is shown. The shape R2 is the ratio of the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point. The first parameter, which is the first weighting factor W1 when the distance to the outermost contour point is R1, and up to what percentage of the distance from the fixed point to the output contour point, the conversion target point and the first conversion point Are determined from at least two parameters including a second parameter that determines whether the coordinates are set to be the same.
[0084]
When the Y coordinate value of the point Pa in FIG. 10 corresponds to the first parameter and the point Pa is positioned on the linear compression / linear expansion function shape limit L11, that is, coincides with the point Pb, linear compression is performed. When the point Pa is positioned on the faithful reproduction / pasting function shape limit L12, that is, coincides with the point Pc, the conversion target point and the first conversion point are the same point in the output contour surface C2, and the output contour surface C2 When it is pasted and Pa is located between them, an intermediate conversion between them is performed.
[0085]
The Y coordinate value of the point Pd in FIG. 10 corresponds to the second parameter, and a first weight function shape L13 of a broken line connecting the origin, the point Pd, the point Pa, and the point (1.0, 1.0) is formed.
[0086]
In addition to these two parameters, another shape parameter may be set to form the first weight function shape of the broken line in more detail than the shape L13.
[0087]
Next, the first weight function shape L13 of the broken line is smoothed to obtain the first weight function shape L14 of the curve, and finally, the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point The actual value R1i of the ratio R1 is input, and the Y coordinate value W1o of the intersection Pe on the first weight function shape L14 of the curve is output as the first weight coefficient W1 of the final output.
[0088]
As an actual process, the first parameter and the second parameter are input to set a plurality of discrete points on the first weight function shape L13 of the polygonal line, and the X coordinate value and Y coordinate value of these points are set. Based on the data pair, the ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point is input by the smoothing parameter and the like according to the method of the invention of the prior application. Therefore, it is not necessary to obtain the first weight function shape L14 of the curve, and the first weight is directly obtained from a plurality of discrete points on the first weight function shape L13 of the polygonal line. The coefficient W1 can be obtained.
[0089]
FIG. 11 shows the shape of a one-variable function when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost contour point is larger than 1, that is, when the extension is performed. Indicates that the third parameter, which is the first weighting factor W1 when the input R1 is 1, and up to what percentage of the distance from the fixed point to the output contour point, the coordinates of the conversion target point and the first conversion point are the same. It is determined from at least two parameters, the above-mentioned second parameter that determines whether or not to set.
[0090]
When the Y coordinate value of the point Pa in FIG. 11 corresponds to the third parameter and the point Pa is located on the linear compression / linear expansion function shape limit L11, that is, coincides with the point Pb, linear expansion is performed. When the point Pa is positioned on the faithful reproduction / paste function shape limit L12, that is, when it coincides with the point Pc, the conversion target point and the first conversion point are the same point, and when Pa is positioned between them, An intermediate conversion between them is performed.
[0091]
The Y coordinate value of the point Pd in FIG. 11 corresponds to the second parameter, and a first weight function shape L13 of a broken line connecting the origin, the point Pd, and the point Pa is formed.
[0092]
In addition to these two parameters, another shape parameter may be set to form the first weight function shape of the broken line in more detail than the shape L13.
[0093]
Next, the first weight function shape L13 of the broken line is smoothed to obtain the first weight function shape L14 of the curve, and finally, the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point The actual value R1i of the ratio R1 is input, and the Y coordinate value W1o of the intersection Pe on the first weight function shape L14 of the curve is output as the first weight coefficient W1 of the final output.
[0094]
As actual processing, the second parameter and the third parameter are input to set a plurality of discrete points on the first weight function shape L13 of the polygonal line, and the X coordinate value and Y coordinate value of these points are set. Based on the data pair, the ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point is input by the smoothing parameter and the like according to the method of the invention of the prior application. Therefore, it is not necessary to obtain the first weight function shape L14 of the curve, and the first weight is directly obtained from a plurality of discrete points on the first weight function shape L13 of the polygonal line. The coefficient W1 can be obtained.
[0095]
Thus, since the methods of FIGS. 10 and 11 use a method similar to compression and decompression, if there is a restriction such as making the second parameter the same, a certain part of the color space region is compressed, Even if a part is expanded, there is an advantage that compression and expansion can be performed without losing continuity therebetween.
[0096]
(Second weighting factor determination step)
In the entire process shown in FIG. 6, next, in the second weighting factor determination step 40, the conversion target point information S1, the fixed point information S0, the input outermost point information S3, and the second weighting factor determination parameter PW2 are used. The second weighting coefficient information SW2 used in the subsequent final conversion point calculation step 70 is calculated.
[0097]
In the second weighting factor determination step 40, a one-variable function calculation is performed with the ratio between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the second weighting factor as an output. To determine the second weighting factor. The shape of the one-variable function will be described with reference to FIG.
[0098]
FIG. 12 shows the shape of a one-variable function of the second weighting factor W2, and the shape is the coordinates of the first transformation point and the final transformation point up to what percentage of the distance from the fixed point to the input outermost point. Set the fourth parameter that determines whether to be the same, and what percentage of the distance from the fixed point to the input outermost point so that the coordinates of the second conversion point and the final conversion point are the same It is determined from at least two parameters with a fifth parameter that determines.
[0099]
The X coordinate value of the point Pf in FIG. 12 corresponds to the fourth parameter, the X coordinate value of the point Pg corresponds to the fifth parameter, the origin, the point Pf, the point Pg, the point (1.0, 1.0 ) To form a second weighting function shape L15.
[0100]
In addition to these two parameters, another shape parameter may be set to form the second weight function shape of the broken line in more detail than the shape L15.
[0101]
Next, the second weight function shape L15 of the broken line is smoothed to obtain the second weight function shape L16 of the curve, and finally, the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point The actual value R1i of the ratio R1 is input, and the Y coordinate value W2o of the intersection Ph on the second weight function shape L16 of the curve is output as the second weight coefficient W2 of the final output.
[0102]
As an actual process, the fourth parameter and the fifth parameter are input, a plurality of discrete points are set on the second weight function shape L15 of the polygonal line, and the X coordinate value and Y coordinate value of these points are set. Based on the data pair, the ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point is input by the smoothing parameter and the like according to the method of the invention of the prior application. Therefore, it is not necessary to obtain the second weight function shape L16 of the curve, and the second weight is directly obtained from a plurality of discrete points on the second weight function shape L15 of the broken line. The coefficient W2 can be obtained.
[0103]
(First conversion point calculation step)
In the entire process shown in FIG. 6, the fixed point P0 and the output contour point P2 are then calculated in the first conversion point calculation step 50 based on the fixed point information S0, the output contour point information S2, and the first weight coefficient information SW1. Is calculated using the first weighting coefficient to calculate the information S4 of the first conversion point P4.
[0104]
Here, the first weighting factor is W1, and GOUTo (ro, θo, φo) is the output contour point information S2 expressed in polar coordinates with the fixed point P0 as the center. Therefore, the first conversion point information S4 is calculated. , GOUTo (ro × W1, θo, φo) is obtained, and if this is returned to the orthogonal coordinates using the coordinate value of the fixed point P0, the first conversion point information S4 is obtained.
[0105]
(Second conversion point calculation step)
In the entire process shown in FIG. 6, next, in a second conversion point calculation process 60, conversion target point information S1, fixed point information S0, output contour surface information SC2, correction data D1, first weight coefficient information SW1, and output Based on the contour point calculation parameter PP2, information S5 of the second conversion point P5 is calculated.
[0106]
The calculation of the second conversion point P5 in the second conversion point calculation step 60 enables the direction of color gamut compression or color gamut expansion to be continuously adjusted for each area, and the visual appearance of the hue line in the color space. In this case, the problem is related to the shape of the color gamut of the output device, for example, the problem that the saturation of yellow is extremely lowered when compressed with the same hue in the case of a printer.
[0107]
FIG. 13 shows a specific example of the second conversion point calculation step 60. The second conversion point P5 is set on the output virtual contour surface C1 obtained by reducing the output contour surface C2 as shown in FIG. Is a case where it is obtained as a point on the output virtual contour surface obtained by enlarging the output contour surface C2.
[0108]
As will be described later, when the color coordinate values of the final conversion points for a plurality of conversion target points obtained by the image processing method of the present invention are stored as grid point data for color gamut conversion in a multidimensional conversion table. Effectively uses the entire color gamut of the actual output device by assuming that the curved surface obtained by enlarging the outermost curved surface of the color gamut of the actual output device is the outermost curved surface of the color gamut of the virtual output device. And avoiding excessive unnecessary color gamut compression.
[0109]
In the example of FIG. 13, first, in the correction data preparation step 61, correction data D1 is prepared based on the output contour surface information SC2 and the input color space visual equal hue information S60.
[0110]
The correction data D1 in this case is for calculating the second conversion point P5, and is correction vector data for discrete chromaticity points over the entire input color space as shown in FIG. It can be obtained by adding the correction vector data obtained from the information SC2 and the correction vector data obtained from the input color space visual equal hue information S60.
[0111]
In order to obtain correction vector data from the input color space visual equi hue information S60, for example, a plurality of saturations are set for the discrete chromaticity points on the baseline L1, and only the hue is moved to the same extent. Whether or not it looks like a hue is obtained by a visual color matching experiment, and using a ratio between the distance from the fixed point P0 to the conversion target point P1 and the distance from the fixed point P0 to the equal color point of the output contour surface C2, the fixed point A virtual correction point on a line from P0 to the color matching point is obtained, a vector from the conversion target point P1 to the virtual correction point is used as a correction vector, and the set saturation point on the line from the fixed point P0 to the color matching point is a fixed point. What is necessary is just to use the method of preservation | save with the information of what percentage corresponds from P0 to the output external curved surface C2.
[0112]
In order to obtain the correction vector data from the output contour surface information SC2, for example, after using the correction vector data determined from the hue information S60 for visual color space visual equivalence, the hue difference, brightness difference, and saturation difference before and after compression are weighted. Various compression samples are created under the condition that the added value becomes the minimum, and the optimum value of the weight ratio is obtained for each region by the sensory test, and is calculated backward from the ratio of the optimum value on the baseline L1. It is possible to use a method for obtaining correction vector data of colors. The same applies to the extension.
[0113]
Further, as a method for obtaining both correction vector data at once, an optimum conversion destination on the output virtual contour surface C1 is obtained by sensory inspection with respect to discrete chromaticity points over the entire input color space, and a correction vector is obtained from the chromaticity. Data can be calculated backwards.
[0114]
A method of directly using a vector from the conversion target point P1 to the second conversion point P5 on the output virtual contour surface C1 as the correction data D1 is also conceivable. However, from the point of prediction accuracy in the next correction value calculation step 62, it is difficult to calculate such that the second transformation point P5 is necessarily on the output virtual contour surface C1, and thus the output contour surface C2 as in this example. It is desirable that the correction data is not easily influenced by the shape of.
[0115]
In the example of FIG. 13, next, in a correction value calculation step 62, correction value information S61 for the conversion target point P1 is calculated based on the correction data D1 and the conversion target point information S1.
[0116]
Here, the first weighting factor is p, the conversion target point P1 is GINo (Lo, ao, bo, p), the origin for calculating the second conversion point P5 and the correction data D1 are GCoe (Loi, aoi, boi, pe), GCce (Lci, aci, bci, pe), i = 1, Nc, the correction value information S61 is calculated using N GCoe (Loi, aoi, boi, pe) and GCce (Lci, aci, Based on the data pair of bci, pe), the predicted value GCoc (Loc, aoc, boc) is predicted as a predicted value GINo (Lo, ao, bo, p). Therefore, it can be calculated using a known method or the method of the prior application.
[0117]
Next, in the correction point calculation step 63, based on the correction value information S61 and the conversion target point S1, information on the conversion target correction point P7 as shown in FIG. S7 is calculated. Specifically, the correction target GCc (Lc, ac, bc) can be obtained by simply adding the correction value GCoc (Loc, aoc, boc) to the conversion target point GINo (Lo, ao, bo). it can.
[0118]
Next, in the second conversion point calculation final step 65, the conversion target correction point information S7, the fixed point information S0, the output outline representative point polar coordinate information S12r obtained in the polar coordinate conversion step 12 of FIG. 7, and the first weight coefficient information SW1. , And the output contour point calculation parameter PP2, as shown in FIG. 1, the second intersection which is the intersection of the correction line L5, which is a line from the fixed point P0 to the conversion target correction point P7, and the output virtual contour surface C1. Information S5 of the conversion point P5 is calculated.
[0119]
Here, when the conversion target correction point GCc (Lc, ac, bc) is converted into polar coordinates centered on the fixed point P0, GCc (rc, θc, φc), the output outline representative point polar coordinates are GOUTi (ri, θi). , Φi), i = 1, Nout, the calculation of the second transformation point P5 that is the intersection of the correction line L5 and the output virtual contour surface C1 is N (θi, φi) and p × ri. This results in the problem of predicting the predicted value (rc) with the predicted value as (θc, φc) based on the data pair. Therefore, it can be calculated using a known method or the method of the prior application.
[0120]
According to the second conversion point calculation step in the example of FIG. 13 described above, the visual curve of the hue line in the color space can be accurately corrected and the problem relating to the shape of the color gamut of the output device can be dealt with. According to the example shown in (2), the second conversion point P5 can be calculated by a simpler method.
[0121]
In the example of FIG. 14, first, in the correction data preparation step 61, correction data D1 is prepared based on the output contour surface information SC2 and the input color space visual equal hue information S60.
[0122]
The correction data D1 in this case is for calculating the input outermost contour correction point P8 as shown in FIG. 2, and further the input outermost contour optimum transformation point P9, and the discrete chromaticity points over the entire input color space. Can be obtained by adding the correction vector data obtained from the output contour surface information SC2 and the correction vector data obtained from the input color space visual equivalent hue information S60.
[0123]
In order to obtain the correction vector data from the hue information S60 such as the visual color space visual equality, for example, how much only the hue is moved with respect to the color on the baseline L1 can be seen as the same hue as the output outline P2. What is necessary is just to use the method of calculating | requiring by the visual color matching experiment and setting the opposite vector.
[0124]
In order to obtain the correction vector data from the output contour surface information SC2, for example, after using the correction vector data determined from the hue information S60 for visual color space visual equivalence, the hue difference, brightness difference, and saturation difference before and after compression are weighted. Various compression samples are created under the condition that the added value becomes the minimum, and the optimum value of the weight ratio is obtained for each region by the sensory test, and is calculated backward from the ratio of the optimum value on the baseline L1. It is possible to use a method for obtaining correction vector data of colors. The same applies to the extension.
[0125]
Further, as a method for obtaining both correction vector data at once, an optimum conversion destination on the output contour surface C2 is obtained by sensory inspection with respect to discrete chromaticity points in the input color space, and correction vector data is obtained from the chromaticity. You can also calculate backwards.
[0126]
Next, in a correction value calculation step 62, correction value information S62 for the input outermost contour point P3 is calculated based on the correction data D1 and the input outermost contour point information S3.
[0127]
Here, the input outermost contour point P3 is GINo (Lo, ao, bo), the input outermost contour optimum transformation point P9 is calculated and the correction data D1 is GCoe (Loi, aoi, boi), GCce (Lci, aci, bci), i = 1, Nc, the correction value information S62 is calculated based on the data pairs of N GCoe (Loi, aoi, boi) and GCce (Lci, aci, bci). Is a GINo (Lo, ao, bo), resulting in a problem of predicting the predicted value GCoc (Loc, aoc, boc). Therefore, it can be calculated using a known method or the method of the prior application.
[0128]
Next, in the correction point calculation step 63, based on the correction value information S62 and the input outermost contour point information S3, the input outermost contour as shown in FIG. Information S8 of the correction point P8 is calculated. Specifically, the input outermost contour correction point GCc (Lc, ac, bc) is obtained by simply adding the correction value GCoc (Loc, aoc, boc) to the input outermost contour point GINo (Lo, ao, bo). be able to.
[0129]
Next, in the input outermost contour optimum transformation point calculation step 64, the input outermost contour correction point information S8, the fixed point information S0, the output contour representative point polar coordinate information S12r obtained in the polar coordinate transformation step 12 of FIG. As shown in FIG. 2, from the calculation parameter PP2, as shown in FIG. 2, the input outermost contour optimal transformation point P9 that is the intersection of the correction line L5 that is a line from the fixed point P0 toward the input outermost contour correction point P8 and the output contour surface C2, that is, the input The information S9 of the optimum conversion destination of the outermost contour point P3 is calculated.
[0130]
Here, when the input outermost contour correction point GCc (Lc, ac, bc) is converted into polar coordinates centered on the fixed point P0, GCc (rc, θc, φc), the output contour representative point polar coordinates are GOUTi (ri, Since θi, φi), i = 1, Nout, the calculation of the input outermost contour optimum transformation point P9 that is the intersection of the correction line L5 and the output contour surface C2 is N (θi, φi) and ri. This results in the problem of predicting the predicted value (rc) with the predicted value as (θc, φc) based on the data pair. Therefore, it can be calculated using a known method or the method of the prior application.
[0131]
Next, in the second conversion point calculation final step 65, based on the input outermost contour conversion point information S9, the fixed point information S0, and the first weight coefficient information SW1, the fixed point P0, the input outermost contour optimal conversion point P9, As shown in FIG. 2, information S5 of the second conversion point P5 on the correction line L5 is calculated.
[0132]
Here, the first weighting factor is W1, and the input outermost contour optimum transformation point P9 expressed in polar coordinates with the fixed point P0 as the center is GCc (rc, θc, φc). Therefore, the second transformation point information S5 Is equivalent to obtaining GCc (rc × W1, θc, φc). If this is returned to the orthogonal coordinates using the coordinate value of the fixed point P0, the second conversion point information S5 is obtained.
[0133]
(Final conversion point calculation process)
In the overall process shown in FIG. 6, finally, in the final conversion point calculation step 70, the first conversion point is based on the first conversion point information S4, the second conversion point information S5, and the second weight coefficient information SW2. By calculating the weighted average of P4 and the second conversion point P5 using the second weighting factor, information S6 of the final conversion point P6 for the conversion target point P1 is calculated.
[0134]
Here, if the second weighting factor is W2, the first conversion point P4 is OUT1 (L1, a1, b1), and the second conversion point P5 is OUT2 (L2, a2, b2), the final conversion point P6 is calculated. Corresponds to obtaining OUT0 (L1 * (1-W2) + L2 * W2, a1 * (1-W2) + a2 * W2, b1 * (1-W2) + b2 * W2).
[0135]
The final conversion point P6 is obtained as a point on a line segment connecting the first conversion point P4 and the second conversion point P5. 1 and 2, the final conversion point curve C6 is a trajectory drawn from the fixed point P0 to the final conversion point P6 when the conversion target point P1 is gradually moved in the direction of the output contour point P2. Is along the line segment from the conversion target point P1 to the output contour point P2, but gradually changes the direction toward the conversion target correction point P7 or the input outermost contour correction point P8 or the input outermost contour optimal conversion point P9. .
[0136]
[ Another image processing method ]
FIG. Another image processing method Indicates. this Method Then, a storage unit 82 is provided for the color gamut conversion processing unit 81, and the storage unit 82 has a visual representation of the hue line for each region of the color space, in this example, for each region of the CIELAB color space. A correction vector for correcting a large bend is stored.
[0137]
Further, the storage means 82 stores information indicating the outermost curved surface of the color gamut of the output device or the output outer curved surface which is a closed curved surface within the color gamut, and the input outermost curved surface of the color gamut of the input image or its partial region. Information indicating the contour surface is stored.
[0138]
Then, the color gamut conversion processing unit 81 uses the output contour surface information and the input outermost contour surface information stored in the storage unit 82 according to the color gamut of the output device and the color gamut of the input image. Ai and bi are subjected to color gamut conversion using the correction vector stored in the storage unit 82, and the converted image data Lo, ao and bo are output.
[0139]
According to this, optimal color gamut conversion according to the color gamut of the output device and the color gamut of the input image can be performed, and in particular, the visual curve of the hue line can be reliably ensured for each area of the color space. It can be corrected.
[0140]
[As an image processing method Second Embodiment of
FIG. 20 shows a color coordinate value of a certain point, that is, a certain pixel of an input image or a partial region thereof as a conversion target point P1, which is on the virtual conversion line L3 and fits the color gamut of the output device. Convert to the final conversion point P25 of the color coordinate value Second 1 shows an embodiment of the present invention.
[0141]
This method includes a correction point calculation step 110, a conversion point calculation step 120, and a reverse correction point calculation step 130.
[0142]
Here, an example for the purpose of performing compression in accordance with the color gamut of the output device in consideration of distortion in the CIELAB color space will be described. Therefore, the virtual conversion line L3 in this example is a uniform hue line.
[0143]
(Correction point calculation process)
In the correction point calculation step 110, the correction point P21 is calculated using the conversion target point P1 and the correction data D2.
[0144]
The correction data D2 in this example is a three-dimensional vector group having correction amounts in the L * direction, a * direction, and b * direction from a plurality of points dispersed in the CIELAB color space.
[0145]
FIG. 21 is a diagram for explaining one correction vector V constituting the correction data D2 for correcting distortion in the CIELAB color space. This figure is shown two-dimensionally by the a * axis and b * axis of the CIELAB color space.
[0146]
The starting point Vs of the correction vector V becomes a conversion target point corresponding to the correction vector V. The end point Ve of the correction vector V is determined by a correction amount for correcting the distortion of the color space for each axial direction.
[0147]
Specifically, if there is a virtual conversion line L3 that has a uniform hue passing through the starting point Vs and a fixed point P0 that can be uniquely determined by the starting point Vs and that has a uniform change in lightness and saturation, this virtual conversion is performed. An intersection point between the line L3 and a closed curved surface called a reference plane C4 is defined as a reference point P26. The reference plane C4 may be any closed surface as long as the half line from the fixed point P0 corresponding to an arbitrary point to the arbitrary point intersects only once. In this example, the output contour surface C2 is used as the closed surface C4.
[0148]
This reference point P26 is a point to be converted when the start point Vs is converted into a point on the reference plane in consideration of distortion of the color space. Therefore, the end point Ve is a point on the straight line L4 passing through the reference point P26 and the fixed point P0 and having a value equal to the saturation of the start point Vs. In this way, correction data D2 based on the reference plane C4 can be created from the virtual conversion line L3.
[0149]
By interpolating the three-dimensional vector group of the correction data D2 created as described above, a correction vector corresponding to the conversion target point P1, that is, a correction vector starting from the conversion target point P1 is determined, and the conversion target point P1 is determined. The correction point P21 is calculated by adding this correction vector.
[0150]
(Conversion point calculation process)
The conversion point calculation step 120 converts the correction point P21 calculated in the correction point calculation step 110 into a point on a half line extending from the fixed point P0 to the correction point P21 in accordance with the color gamut of the output device.
[0151]
In this example, the first weighting factor W1 and the output contour point P23 for the correction point P21 calculated by the input outermost point calculation step, the output contour point calculation step, and the first weighting factor determination step described in detail in the first embodiment. The conversion point P24 is calculated from the fixed point P0. However, in each process, it is necessary to replace the conversion target point P1 with the correction point P21, the first conversion point P4 with the conversion point P24, and the base line L1 with the correction base line. Specifically, the conversion point P24 is calculated by the equation P24 = P0 + W1 × (P23−P0).
[0152]
In this example, a conversion point calculation method that is adaptive to the document was used. Second In this embodiment, as long as the conversion point is calculated on the correction baseline, any compression or expansion method according to the color gamut of the output device may be used. Therefore, the conversion point P24 may always be the output outline point P23, or an existing method may be used.
[0153]
Further, when the difference between the correction base line L2 and the virtual conversion line L3 within the color gamut of the output device is sufficiently small, the conversion point calculated here may be used as the final conversion point. In this case, the next reverse correction point calculation step can be omitted.
[0154]
(Reverse correction point calculation process)
The reverse correction point calculation step 130 calculates the final conversion point by applying the reverse correction data D3 corresponding to the conversion point P24 to the conversion point P24 calculated in the conversion point calculation step 120.
[0155]
The reverse correction data D3 in this example is a three-dimensional vector group having correction amounts in the L * direction, a * direction, and b * direction from a plurality of points dispersed in the CIELAB color space.
[0156]
The reverse correction vector constituting the reverse correction data D3 is equal to the start point and the end point of the correction vector included in the correction data D2. That is, when there is a correction vector V corresponding to the conversion target point Vs included in the correction data D2, the reverse correction vector V ′ = − V corresponding to the conversion point Ve can be added as the reverse correction data D3. Therefore, D2 and D3 can be held as different data as in this example, and the reverse correction data D3 can be substituted by the correction data D2.
[0157]
In this way, the inverse correction vector V ′ corresponding to the conversion point P24 is calculated from the correction data D2 or the inverse correction data D3 by interpolation or prediction processing. Then, the final conversion point P25 is calculated by adding the reverse correction vector V ′ to the conversion point P24.
[0158]
In this example, the final conversion point P25 is an appropriate conversion point of the conversion target point P1, and this second conversion point is defined as the second conversion point described in the first embodiment. It is also possible to configure so as to calculate the final conversion point from the corresponding first conversion point.
[0159]
As described above, according to the method described above, the conversion target point P1 on the virtual conversion line L3 can be accurately converted to the final conversion point P25 on the virtual conversion line L3. In this example, since the virtual conversion line is an equi-hue line, the final conversion point P25 can be calculated while keeping the hue of the conversion target point P1.
[0160]
In this example, it is assumed that the virtual conversion lines are equal hue lines. However, the present invention is not limited to this. For example, the virtual conversion lines for approximating a more preferable color such as a memory color or the like. It is also possible to use a virtual conversion line by combining the above.
[0161]
[Embodiment as Coefficient Generation and Image Processing Apparatus]
In the image processing method shown in FIG. 1 to FIG. 14 and FIG. 18 to FIG. 21, the pixel value of the final conversion point is obtained one by one using the individual pixel values of the input image or its partial area as conversion target points. Although it is possible to perform color gamut conversion on the entire image or its partial area, processing takes time and is not practical.
[0162]
Therefore, in the present invention, a coefficient for color gamut conversion is further generated using the above-described image processing method, and the generated coefficient is mounted on the image processing apparatus so that the input image or its partial area can be quickly and easily To enable color gamut conversion.
[0163]
(First example)
FIG. 16 shows an example of the image processing apparatus of the present invention. In this example, an input image is subjected to color gamut conversion and output to a color printer 93 by a three-dimensional table interpolation calculation unit 91 including a three-dimensional table and an interpolation calculation unit and four one-dimensional tables 92y, 92m, 92c, and 92k. This is the case.
[0164]
All grid point data stored in the three-dimensional table are generated as the above-mentioned final conversion points by using the input values corresponding to the respective grid points as the above conversion target points, and further depending on the output device. In the example, the color space depends on the color printer 93.
[0165]
In order to convert the color space depending on the output device, in this example, since the input 3D space is converted to the output 4D space, it is necessary to reduce one degree of freedom, for example, by determining the method of generating the black plate. .
[0166]
The grid points include points located outside the outermost contour of the color gamut of the input image, and the data obtained as the final conversion points are not all within the color gamut of the output device. Although it is necessary to use a high method, for example, the method of the prior application can be used.
[0167]
The fact that the final conversion point data obtained is not all within the color gamut of the output device is a significant feature of the present invention that it can be extrapolated even to conversion target points outside the color gamut of the input image. Therefore, excessively unnecessary color gamut compression can be avoided.
[0168]
In particular, even when pasting by parameter selection, taking advantage of this extrapolation, a curved surface slightly outside the color gamut outline of the output device, for example, a color gamut range where YMCK% corresponds to −10% to 110% Is assumed to be the color gamut outline of the virtual output device, and the color gamut of the actual output device is faithfully reproduced by using the method of the present invention for generating the grid point data. Compression can be avoided.
[0169]
When the data obtained as the final conversion points by the above-described method is stored in the three-dimensional table of the three-dimensional table interpolation calculation unit 91 as grid point data, as the next step, the one-dimensional tables 92y, 92m, 92c, 92k input / output relationship is set.
[0170]
What is needed in this case is that the final conversion points do not all fall within the color gamut of the output device, that is, the input values of these one-dimensional tables are not necessarily between 0% and 100%. Is to be wider than the output range, and the output range must be within the color gamut of the output device.
[0171]
How wide the input range should be depends on the degree of calculation error and smoothing of the input color gamut. For example, the input range is changed from -10% to 110%, and the output for input values below 0% The value may be set to 0%, the output value for an input value of 100% or more to 100%, and the input value and the output value may be set to be the same during that time. Since an actual color printer has a change with time, the change with time can be absorbed by setting the one-dimensional table.
[0172]
Thereafter, the image processing apparatus may be operated. In this case, the three-dimensional table of the three-dimensional table interpolation calculation unit 91 is indexed by the input image or the data of its partial area, in this example, the upper bits of the L * 'a *' b * 'data, and the grid is extracted from the three-dimensional table. The point data is read, and the read grid point data is interpolated by the lower bits of the input L * 'a *' b * 'data in the interpolation calculation unit, and output from the three-dimensional table interpolation calculation unit 91. Color space data depending on the output device, which is almost adapted to the color gamut of the device, in this example Y′M′C′K ′%, is output.
[0173]
As an interpolation method, a method of performing an interpolation operation by dividing a unit cube into six triangular pyramids, a method of performing an interpolation operation by dividing a unit cube into two triangular prisms, a method of performing interpolation directly on a unit cube, and the like are known. Any of these may be used.
[0174]
In this example, Y′M′C′K ′% from the three-dimensional table interpolation calculation unit 91 is output to the output device that is within the color gamut of the output device by the one-dimensional tables 92y, 92m, 92c, and 92k. Dependent color space data, which in this example is converted to YMCK%.
[0175]
This YMCK% is output to the color printer 93, and the color of the resulting print image, in this example, L * 'a *' b * 'is output.
[0176]
When the input image is a partial area divided and the color processing content is changed for each partial area, the processed partial area data is written in the page memory of the color printer 93, and further, for the next partial area, The processing is performed after changing the lattice point data of the three-dimensional table interpolation calculation unit 91, and when the processing is completed for all the partial areas, the print is finally output.
[0177]
The input color space is not limited to CIELAB, but can be applied to other color spaces such as RGB, XYZ, and YIQ. In addition to color printers, color displays can be applied in the same manner as in color printers except that only three one-dimensional tables are required and the above-described precautions for setting the color gamut of the output device. it can.
[0178]
(Second example)
FIG. 17 shows another example of the image processing apparatus of the present invention. In this example, an input image is subjected to color gamut conversion by the matrix masking unit 95 and the one-dimensional tables 96r, 96g, and 96b and output to the color display 97. In the matrix masking unit 95, a coefficient for arithmetic processing is set. .
[0179]
Therefore, first, assuming that all the grid points in the color gamut of the input image or its partial region are input grid points, the final conversion points are calculated by the method described above using the corresponding input values as conversion target points, The result is converted into a color space depending on the output device as output grid point data, and coefficients are set from the input grid point data and the output grid point data using the least square method.
[0180]
In order to convert the color space depending on the output device, the data obtained as the final conversion point does not all fall within the color gamut of the output device because of the calculation error and the smoothing of the input color gamut described above. It is necessary to use a method with a high extrapolation capability, and for example, the method of the prior application can be used.
[0181]
Next, the input / output relationship of the one-dimensional tables 96r, 96g, 96b is set. What is needed in this case is that the final conversion points do not all fall within the color gamut of the output device, that is, the input values of these one-dimensional tables are not necessarily between 0 and 255, so the input range is output. The output range must be kept within the color gamut of the output device.
[0182]
The extent to which the input range should be increased depends on the degree of calculation error and the degree of smoothing of the input color gamut. For example, the input range is -25 to 280, and the output value for an input value below 0 is 0. In addition, the output value for an input value of 255 or more may be set to 255, and the input value and the output value may be set to be the same during that time. Since an actual color display has a change with time, it is possible to absorb the change with time by setting the one-dimensional table.
[0183]
Thereafter, the image processing apparatus may be operated. In this case, the input image or its partial area data, in this example X′Y′Z ′ data, is subjected to matrix calculation processing by the matrix masking unit 95, and the matrix masking unit 95 substantially matches the color gamut of the output device. Device-dependent color space data, in this example R′G′B ′, is output.
[0184]
There are various calculation methods here, from basic low-order calculations such as a 3x3 matrix to high-order calculations with the addition of high-order terms and cross-term terms, but the required calculation accuracy and calculation cost. It may be selected in consideration of the balance.
[0185]
In this example, R′G′B ′ data from the matrix masking unit 95 is color space data depending on the output device, which is finally contained in the color gamut of the output device by the one-dimensional tables 96r, 96g, and 96b. In the example, it is converted into RGB data.
[0186]
The RGB data is output to the color display 97, and the resulting display display color, in this example, L * 'a *' b * 'is output.
[0187]
When the input image is a partial area divided and the color processing content is changed for each partial area, the processed partial area data is written in the page memory of the color display 97, and further, for the next partial area, Processing is performed after changing the coefficients of the matrix masking section 95, and when the processing is completed for all the partial areas, the display is finally displayed.
[0188]
The input color space is not limited to XYZ, but can be applied to other color spaces such as RGB, CIELAB, and YIQ. Further, not only a color display but also a color printer, the black mask generation function is added to the matrix masking unit 95, the number of one-dimensional tables is four, and the color gamut setting of the output device described above is noted. Except for the difference in points, the present invention can be applied in the same manner as in the case of a color display.
[0189]
【The invention's effect】
According to the present invention, the following effects can be obtained.
[0190]
(1) Depending on the distribution of the input image, only the area that requires color gamut compression can be color gamut compressed. Therefore, the minimum saturation reduction is sufficient, and the color difference from the original is reduced.
[0191]
(2) The direction of color gamut compression can be continuously adjusted for each region. Therefore, it is possible to select an optimum compression direction corresponding to the curve of the hue line of the input color space and the shape of the color gamut of the output device.
[0192]
(3) The amount of color gamut compression can be continuously adjusted including pasting. Therefore, various compression or decompression can be selected simply by changing the parameter according to the user's preference.
[0193]
(4) When color gamut compression is performed by a multi-dimensional DLUT interpolation type color converter, it is possible to avoid excessively unnecessary color gamut compression especially during pasting. Therefore, it is possible to prevent the color difference and the pseudo contour from occurring in the color gamut of the output device.
[Brief description of the drawings]
FIG. 1 is a diagram for explaining an image processing method according to a first invention.
FIG. 2 is a diagram for explaining an image processing method according to the first invention.
FIG. 3 is a diagram illustrating an example of setting a fixed point.
FIG. 4 is a diagram illustrating an example of setting a fixed point.
FIG. 5 is a diagram illustrating an example of setting an output contour surface.
FIG. 6 is a diagram showing a first embodiment of an image processing method according to the present invention.
FIG. 7 is a diagram illustrating an example of an output outline point calculating step.
FIG. 8 is a diagram illustrating an example of an input outermost point calculation step.
FIG. 9 is a diagram illustrating an example of an input outermost point calculation step.
FIG. 10 is a diagram illustrating an example of a function shape used for determining a first weighting factor.
FIG. 11 is a diagram illustrating an example of a function shape used for determining the first weighting factor.
FIG. 12 is a diagram illustrating an example of a function shape used for determining a second weighting factor.
FIG. 13 is a diagram illustrating an example of a second conversion point calculation step.
FIG. 14 is a diagram illustrating an example of a second conversion point calculation step.
FIG. 15 Another image processing method FIG.
FIG. 16 is a diagram showing a first embodiment of an image processing apparatus according to the present invention;
FIG. 17 is a diagram showing an image processing apparatus according to a second embodiment of the present invention.
FIG. 18 Second It is a figure where it uses for description of the image processing method of this invention.
FIG. 19 Second It is a figure where it uses for description of the image processing method of this invention.
FIG. 20 shows an image processing method according to the present invention. Second It is a figure which shows this embodiment.
FIG. 21 Second It is a figure explaining the correction data in the image processing method of this invention.
[Explanation of symbols]
P0 fixed point
P1 conversion point
P2 output outline
P3 Input outermost point
P4 first conversion point
P5 second conversion point
P6 Final conversion point
P21 Correction point
P22 Input outermost point
P23 Output outline point
P24 conversion point
P25 Final conversion point
P26 Correction point
C2 output contour surface
C3 Input outermost curved surface
C4 reference plane
L1 baseline
L2 correction baseline
L3 virtual conversion line
L4 conversion line
L5 correction line
D1 correction data
D2 correction data
D3 Reverse correction data

Claims (17)

In an image processing method for converting a color input image or a partial area thereof according to a color gamut of a color image output device,
A base line, which is a straight line connecting the fixed point in the color gamut of the color image output apparatus uniquely determined from the conversion target point and the conversion target point, and an outermost curved surface of the color gamut of the color image output apparatus or a closed in the color gamut. An output contour point calculating step for obtaining an output contour point that is an intersection with the output contour surface that is a curved surface;
An input outermost point calculation step for obtaining an input outermost point that is an intersection of the baseline and the input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
A weighting factor determination step for determining a first weighting factor and a second weighting factor from the output contour point and the input outermost contour point;
The fixed point, by using the output boundary point and the first weighting factor, the conversion target point by compressing or stretching the point on the base line, and the first conversion point calculation step of calculating the first transformation point,
A correction point calculating step of correcting the conversion target point with correction data corresponding to the conversion target point and calculating a correction point ;
Second conversion point calculation for calculating a second conversion point by compressing or expanding the correction point to a point on a correction line, which is a straight line connecting the correction point and the fixed point, using the first weighting factor. Process,
Using the first conversion point, the second conversion point, and the second weighting factor, a point on a line segment connecting the first conversion point and the second conversion point is converted into a final conversion for the conversion target point. A final conversion point calculation step of calculating as points ,
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting coefficient when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of, determined from at least two parameters,
The second weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the second weighting factor as an output. The shape of the one-variable function is set so that the coordinates of the first transformation point and the final transformation point are the same up to some percent of the distance from the fixed point to the input outermost contour point. And a fifth parameter for determining what percentage or more of the distance from the fixed point to the input outermost point is set so that the coordinates of the second conversion point and the final conversion point are the same. And determined from at least two parameters,
An image processing method. The image processing method according to claim 1 , wherein the second conversion point calculation step includes:
A correction data preparation step for pre-calculating discrete data as the correction data based on the equi-hue information of the input color space and the information of the output contour surface,
Using the calculated correction data, a correction value calculating step of calculating a correction value corresponding to the conversion target point;
A correction point calculating step for obtaining a conversion target correction point by adding the calculated correction value to the conversion target point data;
An output contour representative point preparation step of calculating a plurality of representative points on the output contour surface in advance;
A polar coordinate conversion step for converting the calculated coordinates of the plurality of output contour representative points into polar coordinates centered on the fixed point;
Based on the converted polar coordinates of the plurality of output outline representative points, the second conversion point is reduced by reducing the line from the fixed point to the correction target correction point and the output outline curved surface by the first weighting factor. A second conversion point calculation final step obtained as an intersection with the enlarged output virtual contour surface;
An image processing method comprising: The image processing method according to claim 1 , wherein the second conversion point calculation step includes:
A correction data preparation step for pre-calculating discrete data as the correction data based on the equi-hue information of the input color space and the information of the output contour surface,
Using the calculated correction data, a correction value calculating step for calculating a correction value corresponding to the input outermost contour point;
A correction point calculating step for obtaining an input outermost correction point by adding the calculated correction value to the input outermost point data;
An output contour representative point preparation step of calculating a plurality of representative points on the output contour surface in advance;
Based on the calculated plurality of output contour representative points, an input outermost contour optimal transformation that obtains an input outermost contour optimal transformation point that is an intersection of a line from the fixed point toward the input outermost contour correction point and the output contour surface. A point calculation step;
A second conversion point calculation final step of obtaining the second conversion point by weighted averaging the calculated input outermost contour optimal conversion point and the fixed point using the first weighting factor;
An image processing method comprising: In an image processing method for converting a color input image or a partial area thereof according to a color gamut of a color image output device,
A correction point calculating step of correcting the conversion target point with correction data corresponding to the conversion target point and calculating a correction point;
A correction base line that is a straight line connecting a fixed point in the color gamut of the color image output device and the correction point uniquely determined from the conversion target point, and an outermost curved surface of the color gamut of the color image output device or in the color gamut An output contour point calculating step for obtaining an output contour point that is an intersection with the output contour surface that is a closed curved surface;
An input outermost point calculation step for obtaining an input outermost point that is an intersection of the correction baseline and the input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
A weighting factor determination step of determining a first weighting factor that is a ratio of compressing or expanding the correction point from the output outermost point and the input outermost point;
The fixed point, by using the output boundary point and the first weighting factor, the correction point by compressing or stretching the point on the correction baseline and first conversion point calculation step of calculating the first transformation point,
It said first conversion point is corrected by the inverse correction data corresponding to the first transformation point, and a final conversion point calculation step of calculating a final conversion point for the conversion target point,
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting factor when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of determining at least two parameters,
An image processing method. 5. The image processing method according to claim 1 , wherein the output contour point calculating step includes:
When the color input image or the color gamut of the partial area is known or defined, information on a plurality of representative points on the input outermost curved surface stored in advance and information on the conversion target point An image processing method characterized in that the input outermost contour point is obtained based on the method. 5. The image processing method according to claim 1 , wherein the input outermost point calculation step includes:
A polar coordinate conversion step of converting pixel values of the color input image or the partial area into polar coordinates centered on the fixed point;
An input outermost representative point selection step for obtaining a pixel having the maximum absolute value for each local expected angle of polar coordinates after the conversion as a plurality of representative points on the input outermost contour surface,
Based on the obtained representative point information and the information of the conversion target point, an input outermost point calculation final step for obtaining the input outermost point,
An image processing method comprising: 5. The image processing method according to claim 1 , wherein the input outermost point calculation step includes:
An input outermost contour representative point candidate selection step of selecting a plurality of input outermost contour representative point candidates from the pixel values of the color input image or the partial region;
A polar coordinate conversion step of converting the pixel value of the obtained input outermost contour representative point candidate into polar coordinates centered on the fixed point;
An input outermost representative point selection step for obtaining a pixel having the maximum absolute value for each local expected angle of polar coordinates after the conversion as a plurality of representative points on the input outermost contour surface,
Based on the obtained representative point information and the information of the conversion target point, an input outermost point calculation final step for obtaining the input outermost point,
An image processing method comprising: 5. The image processing method according to claim 1 , wherein the input outermost point calculation step includes:
When the color input image or the color gamut of the partial area is known or defined, information on a plurality of representative points on the input outermost curved surface stored in advance and information on the conversion target point An image processing method characterized in that the input outermost contour point is obtained based on the method. The image processing method according to claim 1 or 4 ,
The correction data is composed of correction vectors corresponding to a plurality of target points in a color space, and each correction vector is an ideal transformation curve passing through the target point and a fixed point uniquely determined by the target point. An image processing method characterized in that the vector is a vector that maps the target point to a point on a half line from the fixed point to the reference point, with an intersection with the reference closed curved surface having the fixed point inside as a reference point. For conversion point a plurality of representative points in the color gamut of the color input image or a partial region, obtains a final conversion point by the image processing method according to claim 1 or 4, the color coordinate value of the obtained plurality of final conversion point Or grid point data stored in a multidimensional conversion table used for color gamut conversion of a color input image or a partial area of the color coordinate value after conversion into a color space depending on the color image output device Lattice point data generation method. A plurality of representative points in the color gamut of the color input image or its partial area are used as conversion target points, a final conversion point is obtained by the image processing method according to claim 1 or 4 , and the obtained conversion target point and final conversion point are obtained. A conversion coefficient generation method for generating a coefficient for color gamut conversion of a color input image or its partial area from the correspondence. In an image processing apparatus that converts a color input image or a partial area thereof according to a color gamut of a color image output apparatus,
A base line, which is a straight line connecting the fixed point in the color gamut of the color image output apparatus uniquely determined from the conversion target point and the conversion target point, and an outermost curved surface of the color gamut of the color image output apparatus or a closed in the color gamut. An output contour point calculating means for obtaining an output contour point that is an intersection with the output contour surface that is a curved surface;
An input outermost point calculation means for obtaining an input outermost point that is an intersection of the base line and an input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
Weighting factor determining means for determining a first weighting factor and a second weighting factor from the output contour point and the input outermost contour point;
The fixed point, by using the output boundary point and the first weighting factor, the conversion target point by compressing or stretching the point on the base line, and the first conversion point calculating means for calculating a first transformation point,
Correction point calculating means for correcting the conversion target point with correction data corresponding to the conversion target point and calculating a correction point ;
Second conversion point calculation for calculating a second conversion point by compressing or expanding the correction point to a point on a correction line, which is a straight line connecting the correction point and the fixed point, using the first weighting factor. Means,
Using the first conversion point, the second conversion point, and the second weighting factor, a point on a line segment connecting the first conversion point and the second conversion point is converted into a final conversion for the conversion target point. A final conversion point calculation means for calculating as points ,
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting coefficient when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of, determined from at least two parameters,
The second weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the second weighting factor as an output. The shape of the one-variable function is set so that the coordinates of the first transformation point and the final transformation point are the same up to some percent of the distance from the fixed point to the input outermost contour point. And a fifth parameter for determining what percentage or more of the distance from the fixed point to the input outermost point is set so that the coordinates of the second conversion point and the final conversion point are the same. And determined from at least two parameters,
An image processing apparatus. In an image processing apparatus that converts a color input image or a partial area thereof according to a color gamut of a color image output apparatus,
Correction point calculation means for correcting the conversion target point with correction data corresponding to the conversion target point and calculating the correction point;
A correction base line that is a straight line connecting a fixed point in the color gamut of the color image output apparatus and the correction point, which is uniquely determined from the conversion target point, and an outermost curved surface of the color gamut of the color image output apparatus or in the color gamut An output contour point calculating means for obtaining an output contour point that is an intersection with the output contour surface that is a closed curved surface;
An input outermost point calculating means for obtaining an input outermost point that is an intersection of the correction baseline and the input outermost curved surface that is the outermost curved surface of the color gamut of the color input image or the partial region;
Weighting factor determination means for determining a first weighting factor that is a ratio of compressing or expanding the correction point from the output contour point and the input outermost contour point;
The fixed point, by using the output boundary point and the first weighting factor, the correction point by compressing or stretching the point on the correction baseline and first conversion point calculating means for calculating a first transformation point,
It said first conversion point is corrected by the inverse correction data corresponding to the first transformation point, and a final conversion point calculation means for calculating a final conversion point for the conversion target point,
The first weighting factor is a one-variable function calculation with a ratio R1 between the distance from the fixed point to the conversion target point and the distance from the fixed point to the input outermost point as an input, and the first weighting factor as an output. The shape of the one-variable function is determined when the ratio R2 between the distance from the fixed point to the output contour point and the distance from the fixed point to the input outermost point is 1 or less. The coordinates of the conversion target point and the first conversion point are the same for the first parameter that is the first weighting coefficient when R2 is the input R1 and up to what percentage of the distance from the fixed point to the output outline point. A third parameter that is a first weighting factor when the input R1 is 1, and when the ratio R2 is greater than 1, The second parameter Of determining at least two parameters,
An image processing apparatus. In an image processing apparatus that converts a color input image or a partial area thereof according to a color gamut of a color image output apparatus,
11. A multidimensional conversion table storing lattice point data generated by the lattice point data generation method according to claim 10 , wherein the multidimensional conversion table is indexed by using pixel values of the color input image or the partial area as addresses. Then, by interpolating the read grid point data using the color input image or the pixel value of the partial area, the output device-dependent color substantially conforming to the color gamut of the color image output device A pre-stage conversion processing unit for obtaining a spatial pixel value;
A post-conversion processing unit composed of a plurality of one-dimensional conversion tables, which finally converts the pixel values obtained from the pre-conversion processing unit into pixel values that fall within the color gamut of the color image output device;
An image processing apparatus comprising: In an image processing apparatus that converts a color input image or a partial area thereof according to a color gamut of a color image output apparatus,
An output device that substantially matches the color gamut of the color image output device by performing a matrix operation on pixel values of the color input image or the partial region using the coefficients generated by the transform coefficient generation method of claim 11 A pre-stage conversion processing unit for obtaining a pixel value of a color space depending on
A post-conversion processing unit composed of a plurality of one-dimensional conversion tables, which finally converts the pixel values obtained from the pre-conversion processing unit into pixel values that fall within the color gamut of the color image output device;
An image processing apparatus comprising: A system apparatus executable on a computer, comprising the image processing apparatus according to claim 12 and causing a printer or display as the color image output apparatus to output a color image. A recording medium used as a storage device of a computer, in which a processing program for performing the image processing method according to claim 1 or 4 is written in a readable state.

Images